TWI796275B - Method for disease risk assessment - Google Patents

Abstract

A method for disease risk assessment includes a capturing step, a preprocessing step, and a determining step. In the capturing step, a medical record of a subject, a static physiological information of the subject measured, and a dynamic physiological information corresponding to different actions of the subject are obtained. In the preprocessing step, a terminal device is applied to integrate the aforementioned data for generating a current statistic. In the determining step, the current statistic is inputted into a prediction model for calculation, so as to generate a disease risk assessment result corresponding to the subject. The assessment result includes a disease category, an onset probability corresponding to the disease category, and an estimated time of the onset of the disease. Thus, the present invention accurately assesses the disease probability of the subject in the future for health improvement and disease prevention and postponement.

Description

Disease Risk Assessment Methods

The invention relates to a technology related to risk assessment, in particular to a disease risk assessment method.

The existing disease risk assessment system analyzes the static physiological data, living habits, and family health history of the subject to obtain the disease risk assessment result based on the subject's health examination report or the questionnaire filled out by the subject.

However, the examination data report obtained from the health examination provided by the health examination center or medical unit can only reflect the current and current health status of the subject, so as to make limited pathological judgments in clinical practice, such as liver function , Abnormal renal function as the basis for further examination, and the normative range of relevant data is often no difference, regardless of gender, age and age, all refer to the same value, for example, the normal range of blood pressure is 120/80mmHg, so it is not easy to be used as an indicator of future health risk Evaluate.

In addition, many early symptoms of the disease will be manifested in changes in body movements and reactions. The testee’s health check data report and questionnaire alone cannot know the abnormalities of the subject’s nervous system, and of course there is no way to assess the risk of related diseases Therefore, it is impossible to improve the health status and prevent the occurrence of diseases in advance.

The main purpose of the present invention is to assess the possibility of a subject suffering from a specific disease in the future through regular and long-term health trend assessment, so as to remind the subject to improve their health status in advance to prevent or delay the occurrence of the disease. In particular, it refers to a method to assess the possibility of a subject suffering from a disease characterized by movement disorders by regularly evaluating the dynamic characteristics of the subject's execution of a specific action, so as to remind the subject to improve their health status early to prevent or delay the occurrence of the disease .

To achieve the above purpose, the present invention provides a disease risk assessment method, which includes an extraction step, a preprocessing step, and a judgment step; in the extraction step, an extraction module is used to obtain a medical Record, a static physiological information measured by the subject and a dynamic physiological information corresponding to different actions. The static physiological information is obtained by measuring the physiological characteristics of the subject when the action is still by lying on a static physiological detection instrument. Dynamic Physiological information is obtained by transmitting multiple action instructions to the subject, and a dynamic physiological detection instrument measures the physiological characteristics of the subject's execution of each action instruction; in the pre-processing step, a terminal device is used to integrate medical records, static Physiological information and dynamic physiological information to generate a current data; in the judgment step, the current data is input into a prediction model for calculation to generate a disease risk assessment result of the subject, the disease risk assessment result is a disease type, corresponding The possibility of onset of the disease type and the estimated time of onset.

Among them, the prediction model is trained and established by inputting the medical records, static physiological information and dynamic physiological information of the subjects known to suffer from diseases, especially the medical records and static physiological information of the pre-diagnosed and diagnosed period. and dynamic physiological information. After a subject who has not yet suffered from a disease is diagnosed with a certain disease, his past and current data will become a new training case for updating the parameters of the prediction model.

Thus, the disease risk assessment method of the present invention extracts the medical records of the subjects to And the current data generated by integrating the static physiological information and dynamic physiological information of the subject, and inputting the current data into the prediction model, can comprehensively analyze the past and current health status of the subject, as well as the subject's Activity and body control ability, so as to obtain the results of the future disease risk assessment of the subject, and then understand the change trend of the subject's future health status, so as to facilitate the early control of the subject's health to improve the physical condition, reduce the risk of disease and Prevent or delay the onset of disease.

100: Evaluation System

10: Terminal device

11: Capture module

12: Processing module

13: Prediction Model

14: Database

20: Static physiological testing equipment

30: Dynamic physiological testing equipment

200: Medical treatment system

S1: Extraction step

S2: Preprocessing steps

S3: Record step

S4: Judgment step

Fig. 1 is a schematic diagram of block connections of an embodiment of the present invention.

Fig. 2 is a flowchart of a disease risk assessment method according to an embodiment of the present invention.

Fig. 3 is a system flow chart of disease risk assessment in the embodiment of the present invention.

In order to illustrate the central idea of the present invention expressed in the column of the above-mentioned summary of the invention, it is expressed in specific embodiments. Various objects in the embodiments are drawn according to proportions, sizes, deformations or displacements suitable for illustration, rather than drawn according to the proportions of actual components, which are described first.

Please refer to FIG. 1 to FIG. 3 , the present invention provides a disease risk assessment method, and the disease risk assessment method of the present invention is implemented through an assessment system 100 .

The evaluation system 100 includes a terminal device 10, a static physiological testing instrument 20, and a dynamic physiological testing instrument 30. As shown in FIG. coupling.

The terminal device 10 can obtain a medical record of a subject, static physiological testing equipment The 20 can measure the physiological characteristics of the subject when the action is static to obtain a static physiological information, and the dynamic physiological detection device 30 can measure the physiological characteristics of the subject when performing corresponding different actions to obtain a dynamic physiological information.

In this embodiment, the terminal device 10 can be various cloud computing devices, personal computers, notebook computers, smart mobile devices or tablet computers, etc.; the static physiological detection instrument 20 can be a blood glucose meter, a blood drug concentration sensor , thermometer, sphygmomanometer, weight scale, height meter, grip meter, vision test meter, hearing test meter, grip meter, tape measure (chest circumference, abdominal circumference, leg circumference measurement), body fat meter, brain wave machine, electrocardiogram machine or Oximeter, etc., can also be combined with a plurality of the above-mentioned instruments as the static physiological detection instrument 20; the dynamic physiological detection instrument 30 can be a thermometer, a sphygmomanometer, an oximeter, an electrocardiogram machine, an electroencephalogram machine, an electromyography sensor, a plantar pressure sensor, etc. A measuring device, an inertial measurement system, a motion capture system, or a photographing device, etc., may also be combined with a plurality of the above-mentioned devices as the dynamic physiological detection device 30 .

In this embodiment, the medical records include the drug pharmacological treatment classification codes and prescriptions of the subject's history of visits, and the medical records also include the subject's diagnosis date, diagnosis disease name, and International Classification of Diseases (ICD-10) of the diagnosis disease name .

In this embodiment, the static physiological information is selected from the blood test value, body temperature, height, weight, height, body fat percentage, grip strength of both hands, chest circumference, abdominal circumference, calf circumference, blood pressure, blood oxygen, vision, Hearing, blood sugar, static electrocardiogram, static electroencephalogram, or a combination thereof.

In this embodiment, the dynamic physiological information includes a plurality of time-domain signals representing different physiological characteristics, and the time-domain signals are selected from continuous body temperature detection, continuous blood pressure detection, continuous blood oxygen detection, ECG signals, and brain wave signals. One or a combination of signals, electromyography signals, plantar pressure signals, trunk movement inertial signals, motion capture signals, and video signals.

The terminal device 10 has a capture module 11, a processing module 12, a preset The test model 13 and a database 14, the retrieval module 11 is networked to the medical treatment system 200 to obtain the medical records of the subject, the retrieval module 11 is connected to the static physiological detection instrument 20 and the dynamic physiological detection instrument 30 by communication Obtaining static physiological information and dynamic physiological information, the processing module 12 integrates the subject's medical records, static physiological information and dynamic physiological information to form current data, and stores the current data and the past of the same subject in the database 14 The data is input into the predictive model 13 to analyze a disease risk assessment result of the subject, wherein the disease risk assessment result is a disease type, the possibility of onset corresponding to the disease type, and the estimated time of onset. The current data will be stored in the database 14 to be used as past data or as updated training data for the future prediction model 13 when the subject conducts a test next time and the prediction model 13 performs analysis.

The above content is to illustrate a specific embodiment of the assessment system 100 of the present invention, and the disease risk assessment method of the present invention is further described below, as shown in Figures 2 to 3, Figure 2 is a flow chart of the disease risk assessment method, and Figure 3 is The system flow chart of disease risk assessment, the disease risk assessment method includes an extraction step S1, a preprocessing step S2, a recording step S3 and a judging step S4.

In the retrieval step S1, the retrieval module 11 is used to connect to the medical treatment system 200 to obtain the latest medical record of the subject, and the static physiological detection instrument 20 is used to measure the subject's static state (such as standing still, Static physiological information is obtained from the physiological characteristics of sitting or lying down), and dynamic physiological information is obtained by using the dynamic physiological detection instrument 30 to measure the physiological characteristics of the subject's execution of each movement instruction by conveying multiple movement instructions to the subject .

In this example, the action instructions conveyed to the subjects were selected from standing on one foot with eyes open, standing on two feet with eyes open, standing on one foot with eyes open, standing on one foot with eyes open, standing on one foot with eyes closed, and standing on two feet with eyes closed. Standing, standing with eyes closed, standing with feet closed, standing with closed eyes and feet, getting up, sitting down, squatting down, walking in a straight line, turning and climbing steps, etc., or a combination of multiple above commands.

In the pre-processing step S2, the processing module 12 of the terminal device 10 integrates the medical records, static physiological information and dynamic physiological information of the subject to generate a current data. The current data is processed by the processing module 12 on the medical records, static Physiological information and dynamic physiological information are generated through synchronous processing and feature analysis.

What is more special is that even if it is a single action command, such as walking in a straight line, it can be regarded as an event when hearing the command to walk forward and taking the first step. In the gait cycle after starting to walk, the left and right feet separate Landing and vacating are also different events. If the time points of each event are not accurately marked, an elderly subject and an early Parkinson's disease patient, the time from hearing the instruction to completing the first step of the two subjects may be The same, but because Parkinson's patients have symptoms of freezing gait (Freezing of gait), they take different times to start, so in addition to being able to mark the difference between the start phase and the left and right gait phases to calculate the time Further, it is necessary to distinguish "start" and "left and right steps" into different events, and mark them on the time signals of brain wave signals, muscle signals, plantar pressure signals and body motion images, so as to target each event during analysis. Dynamic physiological information conducts precise correlation and difference analysis based on different events.

Further explanation, the event synchronization of dynamic physiological information is during the dynamic physiological information measurement phase. When a specific event occurs, the specific event is numbered or coded as an event flag in real time, and the event flag is marked on each dynamic physiological information. The time point when the event flag mark is generated in the time domain signal data measured by the signal measurement equipment is the beginning or end of an instruction, action or special event, so that each time domain signal is analyzed and processed based on the event , the time point of event generation is the same. The way of marking can be manually pressing the event marker, the pre-designed guiding device triggers the event marker synchronously during guidance, or triggers the event marker by a certain dynamic physiological detection instrument 30, and the event marker sends out an event flag Mark the signal for other dynamic physiological detection instruments 30 to receive and mark in their respective records In addition, the processing module 12 may first analyze the signal of a certain dynamic physiological detection instrument 30 as an event flag.

The characteristic analysis of dynamic physiological information further divides the time-domain signal of each physiological information into multiple synchronous blocks according to the sequence of the subject's execution of action instructions according to the event flag of each event. This block can be composed of two events. Defined by the flag, either start from a single event flag and go forward or backward by a fixed number of seconds, or use a single event flag as the middle point, go forward and backward by a fixed number of seconds, and then synchronize according to each The correlation, time difference, periodic change, coordination and center of gravity shift trend between blocks are used to generate complex characteristic indicators. Wherein, the characteristic index is related to the muscular strength, center of gravity transfer ability, balance ability, nerve sensory acuity, nerve coordination and reaction speed of the subject during voluntary actions at each event stage; The blocks respectively include a command receiving block, an action idea block, a start block, an acceleration block, a maintenance block, a deceleration block, a stop block and an idea end block.

It should be noted that the voluntary action comes from the idea of the previous action in the brain's idea area. The action idea will trigger the operation of the motor cortex area of the subject's brain, and trigger the action area of the control muscles to control the corresponding muscle group Generate body movements and myoelectric responses, and receive environmental information sensed by the sensory nervous system through the sensory feedback area, and the operation effect of the overall muscle group will largely reflect the pressure distribution and center of gravity changes on the soles of the feet.

Therefore, the processing module 12 can combine the muscle electrical signals and brain wave electrical signals of the dynamic physiological information to obtain the correlation and time difference between the neuromotor activity of the muscles and the brain cortex, and generate characteristic indicators of voluntary actions at each event stage.

The processing module 12 can also obtain different gait cycle signals from the plantar pressure distribution signals, and compare the muscle electrical signals at the same time to analyze the output and timing of the muscle groups and the shifting trend of the center of gravity generated by the change of plantar pressure, and generate information about the center of gravity. characteristic index.

Taking the Timed Up and Go (TUG) test, which is commonly used to evaluate the sense of balance, as an example, the synchronization processing and feature analysis of the gait of the subject are explained, assuming that the sequence of action instructions conveyed by the subject is from sitting to Start on the chair, "get up", "walk straight" to a cone at 3 meters, then "turn", "walk straight", return to the original position and then "sit down". The dynamic physiological information of the subject undergoing the TUG test is obtained by dynamic physiological detection equipment 30 such as a photography system, EMG-EEG co-tuning equipment, and plantar pressure sensors. The processing module 12 first uses the camera signal Based on the analysis of "getting up", "walking in a straight line", "turning", "sit down", etc., the time point of each action is used as an event flag and marked in other dynamic physiological information signals. The time-domain signals of each physiological feature of the overall dynamic physiological information can be synchronously processed, so that each time-domain signal can provide signals for each stage of "getting up", "walking in a straight line", "turning" and "sitting down" Consistent time points for analysis.

In this embodiment, the processing module 12 further analyzes the time-domain signal of "walking in a straight line" and divides it into a command receiving block, an action idea block, a starting block, an acceleration block, a maintenance block, and a deceleration block , stop block, and thought end block.

Among them, the processing module 12 can reconstruct the image signals of the subject's starting block, acceleration block, maintenance block, deceleration block and stop block into a human skeleton, record the spatial movement of the subject from a visual perspective, and The action area is further divided into two phases: stance phase and swing phase, and combined with EMG signals to obtain the output and timing of muscle groups in different phases to generate characteristic indicators of limb coordination.

In this embodiment, the processing module 12 further combines the myoelectric signals of the subject's action idea block, start block, acceleration block, maintenance block, deceleration block, stop block and idea end block with the brain Electrical signals are used to obtain the correlation between actions and neuromotor activity in the brain cortex, and then generate characteristic indicators about neural sensory acuity, neural coordination and reaction speed.

In this embodiment, the processing module 12 further obtains the transfer trend of the center of gravity of the subject's sole through the plantar pressure signals of the start block, the acceleration block, the maintenance block, the deceleration block, and the stop block. To generate characteristic indicators of center of gravity shift and balance ability.

To further explain, from the control ability of the subject's voluntary actions, the delay time between giving instructions and producing actions, and the degree of coordination between different actions, it is possible to analyze the subject's aging degree or health status, and from the subject's activities It is able to analyze the severity of the subject's movement disorder to assist in the subsequent judgment of disease risk. Therefore, the feature index generated by feature analysis of dynamic physiological information can provide a deeper understanding of the subject's ability to control voluntary body movements and understand the subject's activity, thereby judging the subject's risk of disease.

In the pre-processing step S2, the processing module 12 can further obtain the drug pharmacological treatment classification code and prescription, as well as the name of the diagnosed disease and its International Classification of Diseases code from the medical records of the subject, so as to judge from the subject's medication. Whether it will affect the static physiological information and dynamic physiological information measured by the subject. On the other hand, by obtaining the International Classification of Diseases codes of the drugs used in the medical records, it is possible to determine the type of problem that the subject wants to treat, which can be further used as a basis for subsequent disease risk assessment.

In the recording step S3, the processing module 12 can store the current data generated by the subject through the pre-processing step S2 each time in the database 14 according to the date of integration as the subject's history record.

In the judgment step S4, the processing module 12 inputs the current data of the subject and the history records of the subject at other time points in the past to the prediction model 13 for calculation, and uses the changes in the history records of the subject and the complete history records Data to assist the prediction model13 to generate disease risk assessment results, and avoid the problem of possible misjudgment due to the use of data at a single time point.

For example, the subject is 58 years old this year, since he was 52 years old, he used The assessment system 100 conducts disease risk assessment, assuming that the subject continued to take cold medicine for a cold two weeks before the latest examination, and the latest current data showed that he had abnormal liver function. None of the history records showed any abnormalities in the liver function indicators, so the prediction model 13 can exclude the evaluation of the related diseases caused by the liver function of the subjects to avoid misjudgment.

In addition, after the current data is input into the prediction model 13, at the same time, according to the characteristic weight corresponding to one of the different diseases pre-stored in the database 14, the weight distribution of the current data and the calculation of the possible diseases and the incidence probability are performed, wherein the characteristic weight includes The weighting parameters of the multiple characteristic indexes of the medical record, static physiological information and dynamic physiological information of the current data respectively.

In this embodiment, the distribution principle of feature weights is to first assign weight parameters according to different diseases, and then increase or decrease the distribution of related weight parameters caused by medication data and prescriptions in the subject’s medical records , so as to reduce the interference caused by the influence of drugs on the subjects, so as to carry out more accurate risk assessment of specific diseases.

For example, based on the current technical level, it is known that the factors highly correlated with the symptoms of stroke are "high blood pressure, obesity, cholesterol, and diastolic blood pressure", and the above factors can be appropriately increased for the weight parameters of the judgment of stroke, which meets the The actual situation; in fact, it is also possible to understand this trend through the collection of data through AI artificial intelligence and make appropriate parameter model adjustments by AI artificial intelligence.

In the judgment step S4, the prediction model 13 can further compare the current data with the preset cases of the prediction model 13 to calculate the estimated onset time, so as to generate a disease risk assessment result. By comparing the characteristic index of the current data with the preset case, the difference between the subject and the large group can be obtained according to the big data statistical results of the case, and the estimated onset time of the subject can be estimated from the characteristic level established by the case; In this embodiment, cases are related to specific diseases and specific execution actions, and according to race, country, gender, Age-group subjects were further statistically and classified to establish. For example, take the comparison between the gait characteristics of the subject when walking and the gait preset case about stroke as an example, assuming that the subject is a 66-year-old male in Asia, when the prediction model 13 receives the subject’s current After the data is collected, the walking-related characteristic indicators corresponding to the current data will be compared with the gait cases corresponding to Asian men aged 65 to 70, and the risk and time of possible stroke will be calculated.

In this embodiment, the establishment of the prediction model 13 is based on collecting the dynamic physiological information, static physiological information and medical records of multiple subjects with clear disease classification codes in the medical records, and using a machine learning algorithm And build. In particular, let the predictive model 13 find out the change trend of various medical records, static physiological information and dynamic physiological information before the time point when various diseases are clearly diagnosed. The trend and time course changes of a known disease can be used to evaluate the probability and possible time point of the disease that the subject may suffer from.

The update of the predictive model 13 is to continuously increase the number of subjects who have not yet suffered from the disease, and the medical records obtained by each subject through the retrieval module 11 connected to the medical treatment system 200 will also be detected. Whether the disease classification code item has been updated, for the subjects who have added or updated the disease classification code, their medical records, current data and past detection history records obtained from the database 14 will all be used for the verification of the prediction model 13 , retraining and updating to generate a predictive model 13 that continues to evolve. In particular, after a subject suffers from a major disease, it may not be possible to use the evaluation system 100 for evaluation. Therefore, the database 14 will also regularly retrieve the medical records of all subjects to determine whether any subject has a major illness. Diseases, the data of subjects with such major diseases are also one of the valuable data for the important verification, retraining and parameter update of the prediction model 13.

To further illustrate, the training of the prediction model 13 is combined with case-based reasoning (Case-Based Reasoning) technology, by continuously accumulating the cases of subjects suffering from diseases, using the physiological changes and voluntary movement ability change trends of these subjects with clear disease markers, and increasing the training data of the prediction model 13, so as to achieve continuous evolution prediction Model 13, and gradually improve the accuracy of the prediction model 13.

For example, assume that a 66-year-old Asian female subject is assessed for Parkinson's disease through the evaluation system 100. Parkinson's disease is a chronic neurodegenerative disease affecting the central nervous system. Parkinson's disease onset The precursors to motor function decline and abnormal gait when walking.

In this embodiment, the subject has been recorded and evaluated through the evaluation system 100 every month since he was 63 years old. Therefore, in the retrieval step S1, obtain the latest medical records, current static physiological information and dynamic physiological information of the subject; in the pre-processing step S2, the processing module 12 obtains the subject from the database 14 The tester's past history records, and then integrate the static physiological information and dynamic physiological information of the subject to generate current data; in the recording step S3, the processing module 12 records the current data of the subject to the database 14. In the judgment step S4, the processing module 12 inputs the current data of the subject into the forecasting model 13 for calculation, since the course records include the subject's monthly data from 63 to 66 years old. Take drugs that may cause blood pressure to increase, and have a chronic prescription, and within one year between the ages of 65 and 66, the characteristic indicators of gait have decreased by 2 cm every three months, and the characteristic indicators of the ability to shift the center of gravity There is a downward trend, while the blood pressure has a tendency to increase slowly during the execution of the action command. Through the calculation of the prediction model 13, it can be known that the subject may suffer from Parkinson's disease in the next nine months, and the prevalence rate is 80% . According to the result of the disease risk assessment, the subject can improve physical activity function and quality of life by taking medicine and performing rehabilitation exercise early, so as to delay the occurrence of Parkinson's disease.

To sum up the above, the disease risk assessment method of the present invention captures the subject’s medical records and measures the subject’s static physiological information and dynamic physiological information to integrate the current data, which can be processed by the processing module 12 and predict Model 13 comprehensively analyzes the past and current health status of the subjects condition, as well as the activity and body control ability of the subject, and obtain the disease risk assessment results of the subject, and then understand the change trend of the subject's future health status, so as to facilitate the early control of the subject's health and improve the body condition, reduce the risk of disease and prevent or delay the onset of disease.

In addition, by continuously and massively collecting the data of different subjects, especially the data of subjects diagnosed with new diseases, the prediction model 13 can be continuously trained and verified so that it can continue to evolve and strengthen the prediction model 13. The disease judgment ability allows other subjects who have not yet suffered from the disease to obtain accurate probability of suffering from the disease and the time of occurrence.

The above-mentioned embodiments are only used to illustrate the present invention, and are not intended to limit the scope of the present invention. All modifications or changes that do not violate the spirit of the present invention belong to the intended protection category of the present invention.

10: Terminal device

11: Capture module

12: Processing module

13: Prediction Model

14: Database

20: Static physiological testing equipment

30: Dynamic physiological testing equipment

200: Medical treatment system

Claims (10)

A disease risk assessment method, which comprises the following steps: An extraction step: using an extraction module to obtain a medical record of a subject, a static physiological information measured by the subject, and a dynamic physiological information corresponding to different actions. The static physiological information is obtained through a The static physiological testing instrument measures the physiological characteristics of the subject when the action is still, and the dynamic physiological information is obtained by transmitting multiple action instructions to the subject, and a dynamic physiological testing instrument measures the subject's execution The physiological characteristics of each action command are obtained; A pre-processing step: using a terminal device to integrate the medical record, the static physiological information and the dynamic physiological information to generate a current data; and A judgment step: inputting the current data into a prediction model for calculation to generate a disease risk assessment result of the subject. The disease risk assessment result is a disease type, the incidence probability corresponding to the disease type, and the incidence prognosis estimated time. The disease risk assessment method according to Claim 1, wherein, in the preprocessing step, the terminal device performs feature analysis on the medical record, the static physiological information, and the dynamic physiological information to generate the current data. The disease risk assessment method as described in Claim 2, wherein, in the preprocessing step, the terminal device performs synchronous processing on the dynamic physiological information, and the dynamic physiological information includes multiple times representing different physiological characteristics at the moment when the event occurs signal in the time domain, the terminal device records the event flags in the time domain signal synchronously in a numbered or coded manner. The disease risk assessment method as described in claim 3, wherein the time-domain signals measured by the dynamic physiological detection instrument can be obtained by electrocardiographic sensors, brain wave sensors, myoelectric sensors, plantar One or a combination of ECG signals, electroencephalogram signals, myoelectric signals, plantar pressure signals, and motion characteristic signals measured by pressure sensors, inertial measurement systems, or photographic devices. The method for assessing disease risk according to claim 3, wherein the feature analysis and processing of the dynamic physiological information is further divided into multiple synchronous blocks according to the type or time sequence of events generated during the execution of the action instruction by the subject, A plurality of feature indexes are generated according to the correlation, time difference, period change, coordination and center-of-gravity transfer trend among the synchronization blocks. The disease risk assessment method as described in Claim 1, wherein, in the judging step, after the current data is input into the predictive model, the current data is weighted and calculated according to a feature weight to generate the disease risk assessment result. The disease risk assessment method as described in claim item 1, wherein, the action instruction conveyed to the subject is selected from standing with one foot with eyes open, standing with both feet with eyes open, standing with the back feet with eyes open, standing on tiptoe with eyes open, Standing on one foot with eyes closed, standing on both feet with eyes closed, standing on front and rear feet with eyes closed, standing on tiptoe with eyes closed, standing up, sitting down, squatting down, walking straight, turning and climbing steps, or any combination thereof. The disease risk assessment method as described in Claim 7, wherein the dynamic physiological detection instrument is a plantar pressure sensor, and the plantar pressure sensor is arranged on the insole of the subject, and the subject The action commands conveyed are walking in a straight line, turning and climbing steps. The disease risk assessment method as described in Claim 1, wherein, in the extraction step, the extraction module is network-linked to a medical system to obtain the date of diagnosis and the name of the diagnosed disease in the history of the subject. International Classification of Diseases codes and drug pharmacological treatment classification codes and prescriptions issued by physicians. The disease risk assessment method as described in Claim 9, wherein, after the pre-processing step, a recording step is further included: storing the current data in a database by date as a detection history record of the subject, the The prediction model is established by collecting pathological data and the detection history records of multiple subjects, using a machine learning algorithm for training and verification, and will continue to obtain other medical institutions diagnosed with diseases through a medical treatment system in the future The pathological data of multiple test subjects and the past detection history records of these test subjects are obtained from the database, and the prediction model is updated.

Images